Place recognition for unmanned vehicle based on 3D LiDAR and semantic point cloud
Place recognition is an important task in the computer vision and robotics communities, with a wild application in many fields. For unmanned vehicle, 3D LiDAR and semantic point cloud is always used for place recognition. Recent state-of-the-art works mostly focus on structural design of the network...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/173189 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Place recognition is an important task in the computer vision and robotics communities, with a wild application in many fields. For unmanned vehicle, 3D LiDAR and semantic point cloud is always used for place recognition. Recent state-of-the-art works mostly focus on structural design of the network. This dissertation introduces relation-based and response-based self-knowledge distillation into the training process and further proposes an instance-to-region supervised knowledge distillation method based on MinkLoc3Dv2 backbone. Experimental evaluation shows excellent performance and generalization on standard benchmarks. |
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